Experiment 4: In Table REF  the the influence of \(\lambda \)  on the performance of our model is reported. We achieved the best results for tumor classification and outcome prediction with \(\lambda = 0.3\) . When lowering the value of \(\lambda \)  the model achieves slightly better result for the segmentation for esophageal cancer (0.74) but a worse prediction result for both esophageal and lung (accuracy = 0.70 and AUC = 0.69, accuracy = 0.65 and AUC = 0.64). For \(\lambda = 0.5\)  our model achieves comparable results for both pathologies: accuracy = 0.77 and AUC = 0.76 for esophageal cancer and accuracy = 0.71 and AUC = 0.70 for lung cancer, with a better dice coefficient (0.85) for the segmentation of lung tumors. Increasing \(\lambda \)  does not result in an improvement of the prediction task, it decreases the performance of the segmentation and classification tasks and the prediction: accuracy = 0.73, AUC = 0.71 for esophageal and accuracy = 0.71 and AUC = 0.70 for lung for prediction, accuracy = 0.89 and AUC = 0.88 for classification, and a dice coefficient = 0.69 and 0.78 for esophageal and lung cancers respectively for segmentation.
Experiment 5: Table REF  reports the results of three state-of-the-art methods for multi-task learning for segmentation and classification. Our proposed model achieves the best results for both esophageal and lung cancers for the prediction and classification task, where [1]} achieves a slight improvement on the segmentation task for esophageal cancer (dice coefficient = 0.75), and comparable results for the classification (accuracy = 0.96 and AUC = 0.94).
